After a quick introduction to the potential outcomes framework and a revision of the logic of randomized experiments, we will discuss different matching estimators (propensity score matching, matching on the Mahalanobis distance) and the underlying assumptions that are necessary to obtain causal effects from observational (i.e. non-randomized) studies. All the methods covered in the lectures will be applied in the lab session. All students are welcome to attend, although participants will get the most out of the lecture and lab if they have some prior knowledge of linear regression (at the level of MY452 or equivalent)